Notebook Collections

Author

Harsh Shinde

Published

February 19, 2019

Welcome

This is a collection of data science, machine learning, and artificial intelligence notebooks. Each notebook demonstrates practical implementations using real-world datasets, covering everything from exploratory data analysis to advanced deep learning applications.

What You’ll Find Here

This collection includes diverse data science and machine learning notebooks:

  • Super Bowl Analysis: Analyzing game outcomes, TV viewership, and halftime show performances
  • Netflix Movies Study: Investigating 1990s movie characteristics and trends
  • COVID-19 Vaccines Analysis: Global vaccination data analysis and visualization
  • Cat vs Dog Image Classification: CNN-based image classifier
  • Land Cover Classification: Random Forest classifier for satellite imagery
  • Semantic Segmentation: Deep learning for satellite image segmentation
  • Movie Recommendation System: Content-based recommendation engine
  • Chatbot with NLTK: Web scraping-based conversational agent using natural language processing
  • Bayesian Networks: Probabilistic reasoning for weather prediction
  • Utility Tools: Automation scripts for file transfers and cloud storage integration

About These Notebooks

Each notebook demonstrates:

  • Practical Implementation: Real-world datasets and problems
  • End-to-End Workflows: From data loading to model evaluation
  • Modern Tools: Python, TensorFlow, PyTorch, scikit-learn, pandas, and more
  • Clear Documentation: Step-by-step explanations and visualizations
  • Reproducible Results: Well-documented code in Jupyter Notebooks

Technologies Used

  • Languages: Python
  • Data Analysis: pandas, numpy, matplotlib, seaborn, plotly
  • Machine Learning: scikit-learn, XGBoost
  • Deep Learning: TensorFlow, Keras, PyTorch
  • Computer Vision: OpenCV, rasterio
  • Other Tools: Jupyter Notebooks, Google Colab

Use the sidebar navigation to explore individual projects and notebooks.